Identification of Emotionally Stressful Periods Through Tracking Changes in Statistical Features of mHealth Data

TL;DR

This paper introduces a novel algorithm for detecting periods of emotional stress in older patients by tracking changes in statistical features of passive mobile sensing data, improving accuracy over existing methods.

Contribution

It extends the MOSUM change point detection scheme to identify hotspots of distributional shifts across multiple series without requiring explicit variable relationships.

Findings

Proposed method outperforms benchmarks in simulations.

Hotspot definitions using distance-based statistics and confidence intervals are complementary.

Applied successfully to real patient data to identify stress-related periods.

Abstract

Identifying the onset of emotional stress in older patients with mood disorders and chronic pain is crucial in mental health studies. To this end, studying the associations between passively sensed variables that measure human behaviors and self-reported stress levels collected from mobile devices is emerging. Existing algorithms rely on conventional change point detection (CPD) methods due to the nonstationary nature of the data. They also require explicit modeling of the associations between variables and output only discrete time points, which can lead to misinterpretation of stress onset timings. This is problematic when distributional shifts are complex, dependencies between variables are difficult to capture, and changes occur asynchronously across series with weak signals. In this study, we propose an algorithm that detects hotspots, defined as collections of time intervals during which statistical features of passive sensing variables and stress indicators shift, highlighting periods that require investigation. We first extend the moving sum (MOSUM) scheme to detect simultaneous changes both within and across series, and then define hotspots in two ways: using distance-based test statistics and confidence intervals. The proposed method tracks local changes in combined distributional features, enabling it to capture all types of simultaneous and asynchronous change. It does not require a specific functional relationship between series, and the results are expressed as intervals rather than as individual time points. We conduct simulations under varying signal strengths with mixed and asynchronous distributional shifts, where the proposed method outperforms benchmarks. Results on hotspot identification indicate that the two definitions are complementary. We further apply our method to ALACRITY Phase I data, analyzing hotspots from patients' stress levels and activity measures.